Overview

Dataset statistics

Number of variables27
Number of observations20469
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 MiB
Average record size in memory224.0 B

Variable types

Numeric20
Categorical7

Alerts

source_db has constant value ""Constant
comorbidity_score_name has constant value ""Constant
admission_age is highly overall correlated with comorbidity_score_valueHigh correlation
weight_admission is highly overall correlated with BMI_admissionHigh correlation
BMI_admission is highly overall correlated with weight_admissionHigh correlation
los_hospital is highly overall correlated with los_ICUHigh correlation
los_ICU is highly overall correlated with los_hospitalHigh correlation
comorbidity_score_value is highly overall correlated with admission_ageHigh correlation
pCO2 is highly overall correlated with bmp_bicarbonateHigh correlation
pO2 is highly overall correlated with SaO2 and 1 other fieldsHigh correlation
SaO2 is highly overall correlated with pO2 and 1 other fieldsHigh correlation
SpO2 is highly overall correlated with pO2 and 1 other fieldsHigh correlation
bmp_bicarbonate is highly overall correlated with pCO2High correlation
sex_female is highly overall correlated with GenderHigh correlation
Gender is highly overall correlated with sex_femaleHigh correlation
race_ethnicity is highly imbalanced (56.8%)Imbalance
BMI_admission is highly skewed (γ1 = 51.90390605)Skewed
los_ICU is highly skewed (γ1 = 22.49978938)Skewed
hospital_admission_id has unique valuesUnique
comorbidity_score_value has 1799 (8.8%) zerosZeros
sofa_past_overall_24hr has 2157 (10.5%) zerosZeros

Reproduction

Analysis started2024-01-24 11:04:23.187198
Analysis finished2024-01-24 11:04:43.841534
Duration20.65 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

hospital_admission_id
Real number (ℝ)

UNIQUE 

Distinct20469
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1386586.1
Minimum129260
Maximum2743053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:43.884743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum129260
5-th percentile369838.2
Q1714473
median1233993
Q32191059
95-th percentile2624249.4
Maximum2743053
Range2613793
Interquartile range (IQR)1476586

Descriptive statistics

Standard deviation775876.58
Coefficient of variation (CV)0.55955891
Kurtosis-1.2464559
Mean1386586.1
Median Absolute Deviation (MAD)605719
Skewness0.31892173
Sum2.838203 × 1010
Variance6.0198447 × 1011
MonotonicityNot monotonic
2024-01-24T15:04:43.940906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190805 1
 
< 0.1%
1613651 1
 
< 0.1%
1662691 1
 
< 0.1%
1579858 1
 
< 0.1%
1804387 1
 
< 0.1%
1716119 1
 
< 0.1%
1682319 1
 
< 0.1%
1562471 1
 
< 0.1%
1700148 1
 
< 0.1%
1709399 1
 
< 0.1%
Other values (20459) 20459
> 99.9%
ValueCountFrequency (%)
129260 1
< 0.1%
129341 1
< 0.1%
129397 1
< 0.1%
129534 1
< 0.1%
129887 1
< 0.1%
130186 1
< 0.1%
130270 1
< 0.1%
130343 1
< 0.1%
130585 1
< 0.1%
130702 1
< 0.1%
ValueCountFrequency (%)
2743053 1
< 0.1%
2742956 1
< 0.1%
2742913 1
< 0.1%
2742865 1
< 0.1%
2742860 1
< 0.1%
2742845 1
< 0.1%
2742842 1
< 0.1%
2742694 1
< 0.1%
2742669 1
< 0.1%
2742621 1
< 0.1%

source_db
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size319.8 KiB
eicu
20469 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters81876
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roweicu
2nd roweicu
3rd roweicu
4th roweicu
5th roweicu

Common Values

ValueCountFrequency (%)
eicu 20469
100.0%

Length

2024-01-24T15:04:43.984488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-24T15:04:44.031962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
eicu 20469
100.0%

Most occurring characters

ValueCountFrequency (%)
e 20469
25.0%
i 20469
25.0%
c 20469
25.0%
u 20469
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81876
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20469
25.0%
i 20469
25.0%
c 20469
25.0%
u 20469
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 81876
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20469
25.0%
i 20469
25.0%
c 20469
25.0%
u 20469
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20469
25.0%
i 20469
25.0%
c 20469
25.0%
u 20469
25.0%

admission_age
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.729445
Minimum14
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:44.163230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile34
Q155
median66
Q376
95-th percentile87
Maximum90
Range76
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.583667
Coefficient of variation (CV)0.24075082
Kurtosis0.10240197
Mean64.729445
Median Absolute Deviation (MAD)10
Skewness-0.62729957
Sum1324947
Variance242.85068
MonotonicityNot monotonic
2024-01-24T15:04:44.212969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 616
 
3.0%
67 592
 
2.9%
68 547
 
2.7%
65 544
 
2.7%
71 540
 
2.6%
62 525
 
2.6%
66 519
 
2.5%
72 519
 
2.5%
73 513
 
2.5%
63 509
 
2.5%
Other values (67) 15045
73.5%
ValueCountFrequency (%)
14 1
 
< 0.1%
15 1
 
< 0.1%
16 7
 
< 0.1%
17 7
 
< 0.1%
18 21
 
0.1%
19 39
0.2%
20 37
0.2%
21 45
0.2%
22 59
0.3%
23 79
0.4%
ValueCountFrequency (%)
90 616
3.0%
89 190
 
0.9%
88 214
 
1.0%
87 249
1.2%
86 278
1.4%
85 308
1.5%
84 339
1.7%
83 369
1.8%
82 370
1.8%
81 325
1.6%

sex_female
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size319.8 KiB
0
11127 
1
9342 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20469
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11127
54.4%
1 9342
45.6%

Length

2024-01-24T15:04:44.257936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-24T15:04:44.298116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 11127
54.4%
1 9342
45.6%

Most occurring characters

ValueCountFrequency (%)
0 11127
54.4%
1 9342
45.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20469
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11127
54.4%
1 9342
45.6%

Most occurring scripts

ValueCountFrequency (%)
Common 20469
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11127
54.4%
1 9342
45.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11127
54.4%
1 9342
45.6%

weight_admission
Real number (ℝ)

HIGH CORRELATION 

Distinct2252
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.6001
Minimum0
Maximum639.6
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:44.340333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.9
Q168
median82.32
Q3101.4
95-th percentile140.3
Maximum639.6
Range639.6
Interquartile range (IQR)33.4

Descriptive statistics

Standard deviation30.02945
Coefficient of variation (CV)0.34280156
Kurtosis14.854492
Mean87.6001
Median Absolute Deviation (MAD)16.62
Skewness2.0009873
Sum1793086.4
Variance901.76789
MonotonicityNot monotonic
2024-01-24T15:04:44.388392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 180
 
0.9%
81.6 165
 
0.8%
63.5 158
 
0.8%
90.7 157
 
0.8%
77.1 121
 
0.6%
100 121
 
0.6%
75 118
 
0.6%
80 107
 
0.5%
70 104
 
0.5%
74.8 101
 
0.5%
Other values (2242) 19137
93.5%
ValueCountFrequency (%)
0 1
< 0.1%
0.5 2
< 0.1%
2.5 1
< 0.1%
10 1
< 0.1%
19.8 1
< 0.1%
22 1
< 0.1%
25.2 1
< 0.1%
26.02 1
< 0.1%
27.3 1
< 0.1%
28.4 1
< 0.1%
ValueCountFrequency (%)
639.6 1
< 0.1%
630.9 1
< 0.1%
362.9 1
< 0.1%
303 1
< 0.1%
302.8 1
< 0.1%
295.4 1
< 0.1%
295.1 1
< 0.1%
294.8 1
< 0.1%
287 1
< 0.1%
285.6 1
< 0.1%

height_admission
Real number (ℝ)

Distinct381
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.11547
Minimum1.54
Maximum504.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:44.438816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.54
5-th percentile152.4
Q1162
median170
Q3177.8
95-th percentile187
Maximum504.8
Range503.26
Interquartile range (IQR)15.8

Descriptive statistics

Standard deviation13.135761
Coefficient of variation (CV)0.077673326
Kurtosis67.294791
Mean169.11547
Median Absolute Deviation (MAD)7.8
Skewness-0.85559843
Sum3461624.6
Variance172.54823
MonotonicityNot monotonic
2024-01-24T15:04:44.490561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 1217
 
5.9%
177.8 1166
 
5.7%
167.6 1084
 
5.3%
172.7 1060
 
5.2%
165.1 1042
 
5.1%
162.6 819
 
4.0%
170.2 808
 
3.9%
157.5 760
 
3.7%
175.3 751
 
3.7%
182.9 727
 
3.6%
Other values (371) 11035
53.9%
ValueCountFrequency (%)
1.54 1
 
< 0.1%
1.6 2
< 0.1%
1.65 1
 
< 0.1%
1.67 4
< 0.1%
1.7 2
< 0.1%
1.72 1
 
< 0.1%
1.75 1
 
< 0.1%
1.77 1
 
< 0.1%
1.82 1
 
< 0.1%
15.2 1
 
< 0.1%
ValueCountFrequency (%)
504.8 1
 
< 0.1%
504.2 1
 
< 0.1%
257.5 1
 
< 0.1%
254 1
 
< 0.1%
213.4 1
 
< 0.1%
210.8 1
 
< 0.1%
208.3 2
< 0.1%
205.7 2
< 0.1%
203.2 3
< 0.1%
203 2
< 0.1%

BMI_admission
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct14223
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.54077
Minimum0
Maximum725551.02
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:44.541251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.743844
Q124.017768
median28.480912
Q334.717839
95-th percentile49.058556
Maximum725551.02
Range725551.02
Interquartile range (IQR)10.700071

Descriptive statistics

Standard deviation10083.979
Coefficient of variation (CV)37.691371
Kurtosis3061.5866
Mean267.54077
Median Absolute Deviation (MAD)5.1259043
Skewness51.903906
Sum5476292.1
Variance1.0168662 × 108
MonotonicityNot monotonic
2024-01-24T15:04:44.590793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.60610272 17
 
0.1%
23.29590458 17
 
0.1%
27.35933163 16
 
0.1%
24.01776785 16
 
0.1%
29.93615455 14
 
0.1%
26.539849 14
 
0.1%
24.20811 14
 
0.1%
22.79944302 14
 
0.1%
27.11314372 13
 
0.1%
30.86377727 13
 
0.1%
Other values (14213) 20321
99.3%
ValueCountFrequency (%)
0 1
< 0.1%
0.1581635816 1
< 0.1%
0.1627069755 1
< 0.1%
0.7690393376 1
< 0.1%
2.684215682 1
< 0.1%
3.139042594 1
< 0.1%
3.810394757 1
< 0.1%
7.736827222 1
< 0.1%
9.545817908 1
< 0.1%
9.84375 1
< 0.1%
ValueCountFrequency (%)
725551.0204 1
< 0.1%
657783.7747 1
< 0.1%
588093.2255 1
< 0.1%
385098.0673 1
< 0.1%
321875 1
< 0.1%
283443.4549 1
< 0.1%
279321.5963 1
< 0.1%
263544.7668 1
< 0.1%
260676.2523 1
< 0.1%
237628.4478 1
< 0.1%

los_hospital
Real number (ℝ)

HIGH CORRELATION 

Distinct14919
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.941162
Minimum-0.26458333
Maximum763.275
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size319.8 KiB
2024-01-24T15:04:44.644493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-0.26458333
5-th percentile1.5751389
Q14.6583333
median8.0277778
Q313.819444
95-th percentile28.717639
Maximum763.275
Range763.53958
Interquartile range (IQR)9.1611111

Descriptive statistics

Standard deviation13.307408
Coefficient of variation (CV)1.2162701
Kurtosis842.74204
Mean10.941162
Median Absolute Deviation (MAD)4.0548611
Skewness19.526098
Sum223954.65
Variance177.08711
MonotonicityNot monotonic
2024-01-24T15:04:44.697024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.161111111 7
 
< 0.1%
5.861111111 7
 
< 0.1%
8.05 6
 
< 0.1%
10.12847222 5
 
< 0.1%
4.960416667 5
 
< 0.1%
4.518055556 5
 
< 0.1%
7.338888889 5
 
< 0.1%
5.847222222 5
 
< 0.1%
4.040972222 5
 
< 0.1%
7.238194444 5
 
< 0.1%
Other values (14909) 20414
99.7%
ValueCountFrequency (%)
-0.2645833333 1
< 0.1%
0.09375 1
< 0.1%
0.1291666667 1
< 0.1%
0.1458333333 1
< 0.1%
0.1680555556 1
< 0.1%
0.1722222222 1
< 0.1%
0.1756944444 1
< 0.1%
0.1854166667 1
< 0.1%
0.1881944444 1
< 0.1%
0.1930555556 1
< 0.1%
ValueCountFrequency (%)
763.275 1
< 0.1%
600.7680556 1
< 0.1%
506.5201389 1
< 0.1%
353.8201389 1
< 0.1%
323.9458333 1
< 0.1%
281.0541667 1
< 0.1%
251.9930556 1
< 0.1%
246.9111111 1
< 0.1%
167.5923611 1
< 0.1%
162.5583333 1
< 0.1%

los_ICU
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct800
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1373052
Minimum0
Maximum506.375
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:44.789346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.70833333
Q11.75
median3.2083333
Q36.25
95-th percentile15.833333
Maximum506.375
Range506.375
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation6.9431191
Coefficient of variation (CV)1.35151
Kurtosis1409.1213
Mean5.1373052
Median Absolute Deviation (MAD)1.875
Skewness22.499789
Sum105155.5
Variance48.206903
MonotonicityNot monotonic
2024-01-24T15:04:44.841973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.833333333 221
 
1.1%
1.916666667 218
 
1.1%
2 214
 
1.0%
1 208
 
1.0%
1.75 208
 
1.0%
0.875 208
 
1.0%
1.958333333 202
 
1.0%
1.875 201
 
1.0%
1.041666667 200
 
1.0%
0.9166666667 195
 
1.0%
Other values (790) 18394
89.9%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.04166666667 4
 
< 0.1%
0.08333333333 11
 
0.1%
0.125 25
0.1%
0.1666666667 29
0.1%
0.2083333333 38
0.2%
0.25 51
0.2%
0.2916666667 56
0.3%
0.3333333333 59
0.3%
0.375 56
0.3%
ValueCountFrequency (%)
506.375 1
< 0.1%
246.9166667 1
< 0.1%
98.66666667 1
< 0.1%
87.58333333 1
< 0.1%
82.83333333 1
< 0.1%
81.125 1
< 0.1%
78.20833333 1
< 0.1%
76.54166667 1
< 0.1%
72.625 1
< 0.1%
66.5 1
< 0.1%

comorbidity_score_name
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size319.8 KiB
Charlson
20469 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters163752
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCharlson
2nd rowCharlson
3rd rowCharlson
4th rowCharlson
5th rowCharlson

Common Values

ValueCountFrequency (%)
Charlson 20469
100.0%

Length

2024-01-24T15:04:44.886061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-24T15:04:44.925977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
charlson 20469
100.0%

Most occurring characters

ValueCountFrequency (%)
C 20469
12.5%
h 20469
12.5%
a 20469
12.5%
r 20469
12.5%
l 20469
12.5%
s 20469
12.5%
o 20469
12.5%
n 20469
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 143283
87.5%
Uppercase Letter 20469
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 20469
14.3%
a 20469
14.3%
r 20469
14.3%
l 20469
14.3%
s 20469
14.3%
o 20469
14.3%
n 20469
14.3%
Uppercase Letter
ValueCountFrequency (%)
C 20469
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 163752
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 20469
12.5%
h 20469
12.5%
a 20469
12.5%
r 20469
12.5%
l 20469
12.5%
s 20469
12.5%
o 20469
12.5%
n 20469
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 163752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 20469
12.5%
h 20469
12.5%
a 20469
12.5%
r 20469
12.5%
l 20469
12.5%
s 20469
12.5%
o 20469
12.5%
n 20469
12.5%

comorbidity_score_value
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1252137
Minimum0
Maximum19
Zeros1799
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:44.958310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile9
Maximum19
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7417693
Coefficient of variation (CV)0.66463691
Kurtosis0.70718931
Mean4.1252137
Median Absolute Deviation (MAD)2
Skewness0.70886664
Sum84439
Variance7.5172991
MonotonicityNot monotonic
2024-01-24T15:04:44.997246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 3037
14.8%
3 3024
14.8%
5 2669
13.0%
2 2400
11.7%
6 2071
10.1%
1 1843
9.0%
0 1799
8.8%
7 1419
6.9%
8 884
 
4.3%
9 507
 
2.5%
Other values (10) 816
 
4.0%
ValueCountFrequency (%)
0 1799
8.8%
1 1843
9.0%
2 2400
11.7%
3 3024
14.8%
4 3037
14.8%
5 2669
13.0%
6 2071
10.1%
7 1419
6.9%
8 884
 
4.3%
9 507
 
2.5%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 2
 
< 0.1%
17 3
 
< 0.1%
16 10
 
< 0.1%
15 19
 
0.1%
14 49
 
0.2%
13 74
 
0.4%
12 124
 
0.6%
11 210
1.0%
10 324
1.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size319.8 KiB
0.0
16627 
1.0
3842 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters61407
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 16627
81.2%
1.0 3842
 
18.8%

Length

2024-01-24T15:04:45.038802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-24T15:04:45.079897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16627
81.2%
1.0 3842
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0 37096
60.4%
. 20469
33.3%
1 3842
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40938
66.7%
Other Punctuation 20469
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37096
90.6%
1 3842
 
9.4%
Other Punctuation
ValueCountFrequency (%)
. 20469
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61407
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37096
60.4%
. 20469
33.3%
1 3842
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37096
60.4%
. 20469
33.3%
1 3842
 
6.3%

race_ethnicity
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size319.8 KiB
White
16326 
Black
1746 
Unknown
 
1198
Hispanic OR Latino
 
782
Asian
 
264

Length

Max length31
Median length5
Mean length5.8080512
Min length5

Characters and Unicode

Total characters118885
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWhite
2nd rowWhite
3rd rowWhite
4th rowWhite
5th rowWhite

Common Values

ValueCountFrequency (%)
White 16326
79.8%
Black 1746
 
8.5%
Unknown 1198
 
5.9%
Hispanic OR Latino 782
 
3.8%
Asian 264
 
1.3%
American Indian / Alaska Native 153
 
0.7%

Length

2024-01-24T15:04:45.121689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-24T15:04:45.169145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
white 16326
72.1%
black 1746
 
7.7%
unknown 1198
 
5.3%
hispanic 782
 
3.5%
or 782
 
3.5%
latino 782
 
3.5%
asian 264
 
1.2%
american 153
 
0.7%
indian 153
 
0.7%
153
 
0.7%
Other values (2) 306
 
1.4%

Most occurring characters

ValueCountFrequency (%)
i 19395
16.3%
t 17261
14.5%
e 16632
14.0%
W 16326
13.7%
h 16326
13.7%
n 5881
 
4.9%
a 4339
 
3.6%
k 3097
 
2.6%
c 2681
 
2.3%
2176
 
1.8%
Other values (19) 14771
12.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 93282
78.5%
Uppercase Letter 23274
 
19.6%
Space Separator 2176
 
1.8%
Other Punctuation 153
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 19395
20.8%
t 17261
18.5%
e 16632
17.8%
h 16326
17.5%
n 5881
 
6.3%
a 4339
 
4.7%
k 3097
 
3.3%
c 2681
 
2.9%
o 1980
 
2.1%
l 1899
 
2.0%
Other values (7) 3791
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
W 16326
70.1%
B 1746
 
7.5%
U 1198
 
5.1%
R 782
 
3.4%
L 782
 
3.4%
H 782
 
3.4%
O 782
 
3.4%
A 570
 
2.4%
I 153
 
0.7%
N 153
 
0.7%
Space Separator
ValueCountFrequency (%)
2176
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 116556
98.0%
Common 2329
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 19395
16.6%
t 17261
14.8%
e 16632
14.3%
W 16326
14.0%
h 16326
14.0%
n 5881
 
5.0%
a 4339
 
3.7%
k 3097
 
2.7%
c 2681
 
2.3%
o 1980
 
1.7%
Other values (17) 12638
10.8%
Common
ValueCountFrequency (%)
2176
93.4%
/ 153
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 19395
16.3%
t 17261
14.5%
e 16632
14.0%
W 16326
13.7%
h 16326
13.7%
n 5881
 
4.9%
a 4339
 
3.6%
k 3097
 
2.6%
c 2681
 
2.3%
2176
 
1.8%
Other values (19) 14771
12.4%

pH
Real number (ℝ)

Distinct625
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3567671
Minimum6.683
Maximum7.778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:45.219774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6.683
5-th percentile7.17
Q17.3
median7.37
Q37.425
95-th percentile7.5
Maximum7.778
Range1.095
Interquartile range (IQR)0.125

Descriptive statistics

Standard deviation0.10214859
Coefficient of variation (CV)0.013884984
Kurtosis1.9192191
Mean7.3567671
Median Absolute Deviation (MAD)0.06
Skewness-0.85529075
Sum150585.66
Variance0.010434335
MonotonicityNot monotonic
2024-01-24T15:04:45.268706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.39 658
 
3.2%
7.4 657
 
3.2%
7.41 649
 
3.2%
7.36 620
 
3.0%
7.42 607
 
3.0%
7.38 605
 
3.0%
7.37 600
 
2.9%
7.34 567
 
2.8%
7.43 558
 
2.7%
7.35 551
 
2.7%
Other values (615) 14397
70.3%
ValueCountFrequency (%)
6.683 1
 
< 0.1%
6.742 1
 
< 0.1%
6.8 2
< 0.1%
6.81 1
 
< 0.1%
6.814 1
 
< 0.1%
6.82 2
< 0.1%
6.83 3
< 0.1%
6.84 1
 
< 0.1%
6.848 2
< 0.1%
6.85 1
 
< 0.1%
ValueCountFrequency (%)
7.778 1
< 0.1%
7.75 1
< 0.1%
7.73 1
< 0.1%
7.71 1
< 0.1%
7.705 1
< 0.1%
7.7 1
< 0.1%
7.694 1
< 0.1%
7.69 2
< 0.1%
7.688 1
< 0.1%
7.684 1
< 0.1%

pCO2
Real number (ℝ)

HIGH CORRELATION 

Distinct864
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.065621
Minimum9
Maximum181.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:45.318596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile26
Q134
median40.9
Q349.9
95-th percentile75
Maximum181.7
Range172.7
Interquartile range (IQR)15.9

Descriptive statistics

Standard deviation15.393251
Coefficient of variation (CV)0.34932563
Kurtosis4.2487754
Mean44.065621
Median Absolute Deviation (MAD)7.3
Skewness1.6365666
Sum901979.2
Variance236.95217
MonotonicityNot monotonic
2024-01-24T15:04:45.366784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 512
 
2.5%
39 468
 
2.3%
40 454
 
2.2%
36 441
 
2.2%
37 424
 
2.1%
42 423
 
2.1%
34 422
 
2.1%
41 420
 
2.1%
35 417
 
2.0%
43 404
 
2.0%
Other values (854) 16084
78.6%
ValueCountFrequency (%)
9 1
 
< 0.1%
9.3 1
 
< 0.1%
10 1
 
< 0.1%
11.1 1
 
< 0.1%
11.4 1
 
< 0.1%
11.6 1
 
< 0.1%
11.8 1
 
< 0.1%
12 3
< 0.1%
12.2 1
 
< 0.1%
12.8 2
< 0.1%
ValueCountFrequency (%)
181.7 1
< 0.1%
160.8 1
< 0.1%
147.1 1
< 0.1%
147 2
< 0.1%
145.8 1
< 0.1%
145.6 1
< 0.1%
143.9 1
< 0.1%
141 1
< 0.1%
140 1
< 0.1%
139.8 1
< 0.1%

pO2
Real number (ℝ)

HIGH CORRELATION 

Distinct1197
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.340261
Minimum33
Maximum542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:45.417073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile54
Q168
median81
Q3101.6
95-th percentile166
Maximum542
Range509
Interquartile range (IQR)33.6

Descriptive statistics

Standard deviation43.297457
Coefficient of variation (CV)0.46889034
Kurtosis18.834963
Mean92.340261
Median Absolute Deviation (MAD)15
Skewness3.4579204
Sum1890112.8
Variance1874.6698
MonotonicityNot monotonic
2024-01-24T15:04:45.465923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75 327
 
1.6%
69 323
 
1.6%
74 323
 
1.6%
71 323
 
1.6%
70 321
 
1.6%
77 318
 
1.6%
81 314
 
1.5%
72 309
 
1.5%
68 299
 
1.5%
79 297
 
1.5%
Other values (1187) 17315
84.6%
ValueCountFrequency (%)
33 2
 
< 0.1%
33.5 1
 
< 0.1%
34 2
 
< 0.1%
35 5
< 0.1%
36 7
< 0.1%
37 8
< 0.1%
37.3 1
 
< 0.1%
37.4 1
 
< 0.1%
37.7 1
 
< 0.1%
38 5
< 0.1%
ValueCountFrequency (%)
542 1
< 0.1%
539 1
< 0.1%
529 1
< 0.1%
515 1
< 0.1%
510 2
< 0.1%
509 1
< 0.1%
492 1
< 0.1%
491 1
< 0.1%
486 2
< 0.1%
484.3 1
< 0.1%

SaO2
Real number (ℝ)

HIGH CORRELATION 

Distinct268
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.56087
Minimum70
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:45.514732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile86
Q193
median95.7
Q397.6
95-th percentile99.3
Maximum100
Range30
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation4.3962229
Coefficient of variation (CV)0.046490931
Kurtosis4.718414
Mean94.56087
Median Absolute Deviation (MAD)2.3
Skewness-1.8061122
Sum1935566.4
Variance19.326776
MonotonicityNot monotonic
2024-01-24T15:04:45.562785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 1596
 
7.8%
96 1544
 
7.5%
98 1431
 
7.0%
95 1420
 
6.9%
99 1211
 
5.9%
94 1172
 
5.7%
93 894
 
4.4%
92 723
 
3.5%
100 634
 
3.1%
91 607
 
3.0%
Other values (258) 9237
45.1%
ValueCountFrequency (%)
70 5
< 0.1%
70.1 2
 
< 0.1%
70.2 2
 
< 0.1%
70.3 1
 
< 0.1%
70.4 1
 
< 0.1%
70.5 1
 
< 0.1%
70.6 1
 
< 0.1%
70.7 1
 
< 0.1%
71 8
< 0.1%
71.1 2
 
< 0.1%
ValueCountFrequency (%)
100 634
3.1%
99.9 27
 
0.1%
99.8 36
 
0.2%
99.7 60
 
0.3%
99.6 61
 
0.3%
99.5 43
 
0.2%
99.4 78
 
0.4%
99.3 87
 
0.4%
99.2 75
 
0.4%
99.1 84
 
0.4%

SpO2
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.514046
Minimum70
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:45.700554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile89
Q194
median96
Q398
95-th percentile99
Maximum99
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7539063
Coefficient of variation (CV)0.039302139
Kurtosis7.7751106
Mean95.514046
Median Absolute Deviation (MAD)2
Skewness-2.2444852
Sum1955077
Variance14.091812
MonotonicityNot monotonic
2024-01-24T15:04:45.741738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
99 3905
19.1%
98 3361
16.4%
97 2946
14.4%
96 2439
11.9%
95 1987
9.7%
94 1558
 
7.6%
93 1151
 
5.6%
92 859
 
4.2%
91 619
 
3.0%
90 403
 
2.0%
Other values (20) 1241
 
6.1%
ValueCountFrequency (%)
70 5
 
< 0.1%
71 15
0.1%
72 17
0.1%
73 14
0.1%
74 9
 
< 0.1%
75 17
0.1%
76 12
0.1%
77 26
0.1%
78 26
0.1%
79 17
0.1%
ValueCountFrequency (%)
99 3905
19.1%
98 3361
16.4%
97 2946
14.4%
96 2439
11.9%
95 1987
9.7%
94 1558
 
7.6%
93 1151
 
5.6%
92 859
 
4.2%
91 619
 
3.0%
90 403
 
2.0%

vitals_tempc
Real number (ℝ)

Distinct1363
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.843963
Minimum29.1
Maximum44.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:45.790221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum29.1
5-th percentile35.7
Q136.5
median36.8
Q337.2
95-th percentile38.2
Maximum44.3
Range15.2
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.85174257
Coefficient of variation (CV)0.023117561
Kurtosis8.0361278
Mean36.843963
Median Absolute Deviation (MAD)0.4
Skewness-0.95015244
Sum754159.08
Variance0.72546541
MonotonicityNot monotonic
2024-01-24T15:04:45.840721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.8 1330
 
6.5%
36.6 1291
 
6.3%
36.7 1276
 
6.2%
36.9 1136
 
5.5%
36.4 1044
 
5.1%
37 940
 
4.6%
37.1 852
 
4.2%
37.2 831
 
4.1%
36.5 824
 
4.0%
36.3 680
 
3.3%
Other values (1353) 10265
50.1%
ValueCountFrequency (%)
29.1 1
< 0.1%
29.27777778 1
< 0.1%
29.3 1
< 0.1%
29.7 1
< 0.1%
30.1 1
< 0.1%
30.2 1
< 0.1%
30.4 1
< 0.1%
30.5 2
< 0.1%
30.6 1
< 0.1%
30.7 1
< 0.1%
ValueCountFrequency (%)
44.3 1
 
< 0.1%
41.1 1
 
< 0.1%
40.9 2
 
< 0.1%
40.5 3
< 0.1%
40.4 1
 
< 0.1%
40.3 5
< 0.1%
40.281 1
 
< 0.1%
40.27 1
 
< 0.1%
40.2 2
 
< 0.1%
40.1 6
< 0.1%

cbc_hemoglobin
Real number (ℝ)

Distinct651
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.409057
Minimum3
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:45.894775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7.8
Q19.8
median11.3
Q312.9
95-th percentile15.4
Maximum23
Range20
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation2.3169005
Coefficient of variation (CV)0.20307555
Kurtosis0.14813445
Mean11.409057
Median Absolute Deviation (MAD)1.6
Skewness0.31941049
Sum233531.98
Variance5.3680279
MonotonicityNot monotonic
2024-01-24T15:04:45.947179image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.9 331
 
1.6%
11.2 330
 
1.6%
10.2 309
 
1.5%
11.5 303
 
1.5%
10.5 300
 
1.5%
11.1 297
 
1.5%
10.3 295
 
1.4%
10.4 293
 
1.4%
12.1 290
 
1.4%
10.8 287
 
1.4%
Other values (641) 17434
85.2%
ValueCountFrequency (%)
3 1
 
< 0.1%
3.6 1
 
< 0.1%
4.1 2
< 0.1%
4.4 2
< 0.1%
4.5 3
< 0.1%
4.6 1
 
< 0.1%
4.7 1
 
< 0.1%
4.8 2
< 0.1%
4.9 3
< 0.1%
5 3
< 0.1%
ValueCountFrequency (%)
23 1
< 0.1%
22.8 1
< 0.1%
22.4 1
< 0.1%
21.6 1
< 0.1%
21.4 1
< 0.1%
21.1 1
< 0.1%
21 1
< 0.1%
20.9 1
< 0.1%
20.6 1
< 0.1%
20.4 2
< 0.1%

bmp_sodium
Real number (ℝ)

Distinct208
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.42484
Minimum100
Maximum183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:46.004987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile130
Q1136
median138.8
Q3141
95-th percentile147
Maximum183
Range83
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.4264491
Coefficient of variation (CV)0.039201412
Kurtosis3.5108347
Mean138.42484
Median Absolute Deviation (MAD)2.8
Skewness-0.074306694
Sum2833418.1
Variance29.44635
MonotonicityNot monotonic
2024-01-24T15:04:46.054895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 1687
 
8.2%
139 1656
 
8.1%
140 1630
 
8.0%
137 1479
 
7.2%
141 1409
 
6.9%
136 1326
 
6.5%
142 1194
 
5.8%
135 983
 
4.8%
143 930
 
4.5%
134 852
 
4.2%
Other values (198) 7323
35.8%
ValueCountFrequency (%)
100 1
 
< 0.1%
106 1
 
< 0.1%
108 2
 
< 0.1%
109 5
< 0.1%
110 3
 
< 0.1%
111 3
 
< 0.1%
112 5
< 0.1%
113 3
 
< 0.1%
114 4
< 0.1%
115 8
< 0.1%
ValueCountFrequency (%)
183 1
 
< 0.1%
175 1
 
< 0.1%
174 1
 
< 0.1%
173 2
< 0.1%
172 1
 
< 0.1%
171 2
< 0.1%
170 2
< 0.1%
169 1
 
< 0.1%
168 4
< 0.1%
166 1
 
< 0.1%

bmp_bicarbonate
Real number (ℝ)

HIGH CORRELATION 

Distinct512
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.62832
Minimum2
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:46.105336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15
Q121
median24
Q328
95-th percentile36
Maximum63
Range61
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.1024743
Coefficient of variation (CV)0.24778281
Kurtosis1.4014193
Mean24.62832
Median Absolute Deviation (MAD)3
Skewness0.44627359
Sum504117.08
Variance37.240193
MonotonicityNot monotonic
2024-01-24T15:04:46.153940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 1426
 
7.0%
23 1353
 
6.6%
25 1322
 
6.5%
22 1203
 
5.9%
26 1129
 
5.5%
21 1019
 
5.0%
27 971
 
4.7%
20 864
 
4.2%
28 812
 
4.0%
19 739
 
3.6%
Other values (502) 9631
47.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 1
 
< 0.1%
4 4
 
< 0.1%
5 8
 
< 0.1%
6 16
 
0.1%
7 21
 
0.1%
8 40
0.2%
8.2 1
 
< 0.1%
8.7 1
 
< 0.1%
9 62
0.3%
ValueCountFrequency (%)
63 1
 
< 0.1%
58 1
 
< 0.1%
57 1
 
< 0.1%
55 1
 
< 0.1%
54.6 1
 
< 0.1%
53 3
 
< 0.1%
52 1
 
< 0.1%
51 2
 
< 0.1%
50 6
< 0.1%
49 8
< 0.1%

bmp_creatinine
Real number (ℝ)

Distinct1877
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5756026
Minimum0.1
Maximum22.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:46.204554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.51
Q10.8
median1.09
Q31.71
95-th percentile4.28
Maximum22.99
Range22.89
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation1.532986
Coefficient of variation (CV)0.9729522
Kurtosis24.690207
Mean1.5756026
Median Absolute Deviation (MAD)0.38
Skewness4.0296056
Sum32251.01
Variance2.3500461
MonotonicityNot monotonic
2024-01-24T15:04:46.252992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 455
 
2.2%
0.7 435
 
2.1%
0.9 386
 
1.9%
1 374
 
1.8%
1.1 319
 
1.6%
0.6 314
 
1.5%
1.2 260
 
1.3%
1.3 232
 
1.1%
0.5 212
 
1.0%
1.4 200
 
1.0%
Other values (1867) 17282
84.4%
ValueCountFrequency (%)
0.1 4
< 0.1%
0.11 1
 
< 0.1%
0.14 1
 
< 0.1%
0.15 2
 
< 0.1%
0.17 3
 
< 0.1%
0.18 2
 
< 0.1%
0.19 2
 
< 0.1%
0.2 9
< 0.1%
0.21 3
 
< 0.1%
0.22 3
 
< 0.1%
ValueCountFrequency (%)
22.99 1
< 0.1%
20.22 1
< 0.1%
20.01 1
< 0.1%
19.35 1
< 0.1%
18.8 1
< 0.1%
18.51 1
< 0.1%
18.43 1
< 0.1%
18.2 1
< 0.1%
17.62 1
< 0.1%
17.56 1
< 0.1%

sofa_past_overall_24hr
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5660755
Minimum0
Maximum24
Zeros2157
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:46.294795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile11
Maximum24
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3298993
Coefficient of variation (CV)0.72926944
Kurtosis0.5725895
Mean4.5660755
Median Absolute Deviation (MAD)2
Skewness0.75779203
Sum93463
Variance11.08823
MonotonicityNot monotonic
2024-01-24T15:04:46.336854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4 2565
12.5%
5 2328
11.4%
3 2206
10.8%
1 2170
10.6%
0 2157
10.5%
6 1943
9.5%
2 1867
9.1%
7 1518
7.4%
8 1236
6.0%
9 803
 
3.9%
Other values (15) 1676
8.2%
ValueCountFrequency (%)
0 2157
10.5%
1 2170
10.6%
2 1867
9.1%
3 2206
10.8%
4 2565
12.5%
5 2328
11.4%
6 1943
9.5%
7 1518
7.4%
8 1236
6.0%
9 803
 
3.9%
ValueCountFrequency (%)
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 5
 
< 0.1%
19 7
 
< 0.1%
18 12
 
0.1%
17 16
 
0.1%
16 36
0.2%
15 72
0.4%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size319.8 KiB
1.0
10719 
0.0
7694 
4.0
1189 
3.0
 
579
2.0
 
288

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters61407
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 10719
52.4%
0.0 7694
37.6%
4.0 1189
 
5.8%
3.0 579
 
2.8%
2.0 288
 
1.4%

Length

2024-01-24T15:04:46.375434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-24T15:04:46.419761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10719
52.4%
0.0 7694
37.6%
4.0 1189
 
5.8%
3.0 579
 
2.8%
2.0 288
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 28163
45.9%
. 20469
33.3%
1 10719
 
17.5%
4 1189
 
1.9%
3 579
 
0.9%
2 288
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40938
66.7%
Other Punctuation 20469
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28163
68.8%
1 10719
 
26.2%
4 1189
 
2.9%
3 579
 
1.4%
2 288
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 20469
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61407
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28163
45.9%
. 20469
33.3%
1 10719
 
17.5%
4 1189
 
1.9%
3 579
 
0.9%
2 288
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28163
45.9%
. 20469
33.3%
1 10719
 
17.5%
4 1189
 
1.9%
3 579
 
0.9%
2 288
 
0.5%

p50
Real number (ℝ)

Distinct4590
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.457867
Minimum10.024611
Maximum99.871457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size319.8 KiB
2024-01-24T15:04:46.466119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10.024611
5-th percentile15.238877
Q120.589182
median24.301985
Q328.631162
95-th percentile45.973891
Maximum99.871457
Range89.846846
Interquartile range (IQR)8.0419795

Descriptive statistics

Standard deviation10.943854
Coefficient of variation (CV)0.41363328
Kurtosis11.88887
Mean26.457867
Median Absolute Deviation (MAD)3.9667564
Skewness2.8916717
Sum541566.08
Variance119.76795
MonotonicityNot monotonic
2024-01-24T15:04:46.514569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.08244352 71
 
0.3%
21.36139503 67
 
0.3%
20.4663275 65
 
0.3%
19.75238584 64
 
0.3%
23.02591932 63
 
0.3%
21.63881574 62
 
0.3%
22.47107789 62
 
0.3%
20.52913289 62
 
0.3%
22.7484986 62
 
0.3%
24.97704049 61
 
0.3%
Other values (4580) 19830
96.9%
ValueCountFrequency (%)
10.0246109 1
 
< 0.1%
10.09805128 7
< 0.1%
10.18985174 1
 
< 0.1%
10.26456644 2
 
< 0.1%
10.28165221 5
< 0.1%
10.31837239 1
 
< 0.1%
10.37595208 1
 
< 0.1%
10.46525314 4
< 0.1%
10.4711443 1
 
< 0.1%
10.53869351 1
 
< 0.1%
ValueCountFrequency (%)
99.87145728 1
< 0.1%
99.71385141 1
< 0.1%
99.61775392 1
< 0.1%
99.59403657 1
< 0.1%
99.5117053 1
< 0.1%
99.4007947 1
< 0.1%
99.36637287 1
< 0.1%
99.26329914 1
< 0.1%
99.11349358 1
< 0.1%
98.9609025 1
< 0.1%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size319.8 KiB
Male
11127 
Female
9342 

Length

Max length6
Median length4
Mean length4.912795
Min length4

Characters and Unicode

Total characters100560
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 11127
54.4%
Female 9342
45.6%

Length

2024-01-24T15:04:46.560225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-24T15:04:46.606099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
male 11127
54.4%
female 9342
45.6%

Most occurring characters

ValueCountFrequency (%)
e 29811
29.6%
a 20469
20.4%
l 20469
20.4%
M 11127
 
11.1%
F 9342
 
9.3%
m 9342
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80091
79.6%
Uppercase Letter 20469
 
20.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 29811
37.2%
a 20469
25.6%
l 20469
25.6%
m 9342
 
11.7%
Uppercase Letter
ValueCountFrequency (%)
M 11127
54.4%
F 9342
45.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 100560
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 29811
29.6%
a 20469
20.4%
l 20469
20.4%
M 11127
 
11.1%
F 9342
 
9.3%
m 9342
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 29811
29.6%
a 20469
20.4%
l 20469
20.4%
M 11127
 
11.1%
F 9342
 
9.3%
m 9342
 
9.3%

Interactions

2024-01-24T15:04:42.660668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:24.545300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:25.574161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:26.486273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:27.485364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:28.369619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:29.294772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.303562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.171103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:32.130770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.089567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.972182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:35.168524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.242380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.131032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:38.042898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.072968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.928065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:40.819878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:41.763301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:42.703690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:24.645667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:25.620562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:26.530122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:27.530681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:28.416483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:29.340713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.346100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.223322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:32.173633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.132900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:34.014051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:35.248926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.286674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.178453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:38.089994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.114918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.972706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:40.862736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:41.806422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:42.747023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:24.716947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:25.668623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:26.577342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:27.577695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:28.465433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:29.388546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.391813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.272035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:32.218810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.179966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:34.058644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:35.309995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.332204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.226355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2024-01-24T15:04:40.463123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:41.337221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:42.315334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:43.217829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:25.269317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:26.172412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:27.073373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:28.068954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:28.974635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:29.986005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.869421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.807926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:32.790373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.667653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:34.595598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:35.807169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.826954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.730955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:38.753281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.633557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:40.508171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:41.379439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:42.359382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:43.261548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:25.314716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:26.219687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:27.209969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:28.113717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:29.023511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.033406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.914553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.856387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:32.835603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.713804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:34.751566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:35.985556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.872746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.777160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:38.801690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.677265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:40.553949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:41.423925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:42.405153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:43.307168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:25.363262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:26.268671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:27.258904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:28.161614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:29.075458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.082955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.963664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.905437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:32.881872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.761323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:34.821981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.033577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.921812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.825566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:38.850719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.724529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:40.604851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:41.562375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:42.452832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:43.347306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:25.404386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:26.311959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:27.300168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:28.202454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:29.118735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.125619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.004117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.950240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:32.923145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.802527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:34.879794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.074074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.963277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.869126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:38.894356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.763891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:40.649176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:41.601829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:42.494770image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:43.389462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:25.449248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:26.357005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:27.345436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:28.246267image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:29.164897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.172510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.048616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.997346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:32.968201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.846433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:34.971239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.118856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.006817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.913929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:38.940764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.807412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:40.694693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:41.643969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:42.538019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:43.429816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:25.490260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:26.400180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:27.386973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:28.286810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:29.207164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.215030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.089175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:32.041242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.008385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.888736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:35.048585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.159295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.047640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.956317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:38.984539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.846896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:40.735518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:41.682938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:42.579123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:43.471558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:25.533522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:26.444437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:27.438086image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:28.329928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:29.252698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:30.260288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:31.131896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:32.086885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.049739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:33.931050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:35.099667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:36.202337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:37.091269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:38.000156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.029433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:39.889361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:40.778323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:41.724004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-24T15:04:42.620850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-01-24T15:04:46.655970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
hospital_admission_idadmission_ageweight_admissionheight_admissionBMI_admissionlos_hospitallos_ICUcomorbidity_score_valuepHpCO2pO2SaO2SpO2vitals_tempccbc_hemoglobinbmp_sodiumbmp_bicarbonatebmp_creatininesofa_past_overall_24hrp50sex_femalein_hospital_mortalityrace_ethnicitysofa_past_cardiovascular_24hrGender
hospital_admission_id1.000-0.0540.0200.0060.015-0.0070.051-0.013-0.0250.028-0.042-0.100-0.0220.0310.020-0.0430.018-0.007-0.082-0.0200.0060.0500.1540.0890.006
admission_age-0.0541.000-0.200-0.139-0.1510.010-0.0200.6830.049-0.023-0.0150.018-0.029-0.108-0.1500.0580.0570.1380.0470.0040.0740.1320.0710.0560.074
weight_admission0.020-0.2001.0000.3960.8910.0250.026-0.099-0.0610.149-0.005-0.025-0.0330.0500.108-0.0370.0900.1570.0000.0320.2580.0640.0420.0280.258
height_admission0.006-0.1390.3961.000-0.0140.0050.016-0.0920.018-0.0190.0240.0190.0080.0170.124-0.021-0.0170.1070.0200.0190.2580.0080.0370.0090.258
BMI_admission0.015-0.1510.891-0.0141.0000.0260.021-0.064-0.0720.163-0.016-0.035-0.0380.0500.060-0.0320.1000.125-0.0080.0260.0030.0130.0230.0000.003
los_hospital-0.0070.0100.0250.0050.0261.0000.6700.0100.084-0.015-0.020-0.004-0.0080.091-0.1670.0050.0220.0150.043-0.0100.0070.0040.0170.0000.007
los_ICU0.051-0.0200.0260.0160.0210.6701.000-0.0230.038-0.008-0.041-0.038-0.0480.087-0.0970.0300.0010.0130.0790.0190.0040.0160.0010.0060.004
comorbidity_score_value-0.0130.683-0.099-0.092-0.0640.010-0.0231.0000.0200.017-0.045-0.039-0.040-0.104-0.226-0.0230.0640.2580.067-0.0140.0570.1410.0340.0500.057
pH-0.0250.049-0.0610.018-0.0720.0840.0380.0201.000-0.419-0.0960.1490.0820.158-0.0460.0140.223-0.217-0.077-0.2000.0450.2260.0290.0810.045
pCO20.028-0.0230.149-0.0190.163-0.015-0.0080.017-0.4191.000-0.053-0.160-0.117-0.0750.0980.0460.529-0.111-0.1090.0880.0500.0840.0290.0470.050
pO2-0.042-0.015-0.0050.024-0.016-0.020-0.041-0.045-0.096-0.0531.0000.8710.5720.0130.0440.018-0.1130.0120.0280.4260.0180.0440.0120.0180.018
SaO2-0.1000.018-0.0250.019-0.035-0.004-0.038-0.0390.149-0.1600.8711.0000.5760.0420.0260.037-0.058-0.0460.0130.2870.0180.1100.0180.0340.018
SpO2-0.022-0.029-0.0330.008-0.038-0.008-0.048-0.0400.082-0.1170.5720.5761.0000.019-0.0220.042-0.059-0.0260.025-0.4170.0000.1440.0250.0290.000
vitals_tempc0.031-0.1080.0500.0170.0500.0910.087-0.1040.158-0.0750.0130.0420.0191.0000.0050.0170.039-0.0630.0340.0020.0290.1660.0250.0510.029
cbc_hemoglobin0.020-0.1500.1080.1240.060-0.167-0.097-0.226-0.0460.0980.0440.026-0.0220.0051.000-0.0330.089-0.159-0.1560.0920.1450.0850.0290.0950.145
bmp_sodium-0.0430.058-0.037-0.021-0.0320.0050.030-0.0230.0140.0460.0180.0370.0420.017-0.0331.0000.087-0.0650.075-0.0270.0180.0790.0140.0420.018
bmp_bicarbonate0.0180.0570.090-0.0170.1000.0220.0010.0640.2230.529-0.113-0.058-0.0590.0390.0890.0871.000-0.298-0.218-0.0460.0710.1750.0320.1090.071
bmp_creatinine-0.0070.1380.1570.1070.1250.0150.0130.258-0.217-0.1110.012-0.046-0.026-0.063-0.159-0.065-0.2981.0000.3020.0290.0620.1180.0530.0710.062
sofa_past_overall_24hr-0.0820.0470.0000.020-0.0080.0430.0790.067-0.077-0.1090.0280.0130.0250.034-0.1560.075-0.2180.3021.0000.0010.0390.2040.0190.3320.039
p50-0.0200.0040.0320.0190.026-0.0100.019-0.014-0.2000.0880.4260.287-0.4170.0020.092-0.027-0.0460.0290.0011.0000.0030.1120.0420.0310.003
sex_female0.0060.0740.2580.2580.0030.0070.0040.0570.0450.0500.0180.0180.0000.0290.1450.0180.0710.0620.0390.0031.0000.0000.0110.0461.000
in_hospital_mortality0.0500.1320.0640.0080.0130.0040.0160.1410.2260.0840.0440.1100.1440.1660.0850.0790.1750.1180.2040.1120.0001.0000.0210.1830.000
race_ethnicity0.1540.0710.0420.0370.0230.0170.0010.0340.0290.0290.0120.0180.0250.0250.0290.0140.0320.0530.0190.0420.0110.0211.0000.0310.011
sofa_past_cardiovascular_24hr0.0890.0560.0280.0090.0000.0000.0060.0500.0810.0470.0180.0340.0290.0510.0950.0420.1090.0710.3320.0310.0460.1830.0311.0000.046
Gender0.0060.0740.2580.2580.0030.0070.0040.0570.0450.0500.0180.0180.0000.0290.1450.0180.0710.0620.0390.0031.0000.0000.0110.0461.000

Missing values

2024-01-24T15:04:43.555968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-24T15:04:43.735782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hospital_admission_idsource_dbadmission_agesex_femaleweight_admissionheight_admissionBMI_admissionlos_hospitallos_ICUcomorbidity_score_namecomorbidity_score_valuein_hospital_mortalityrace_ethnicitypHpCO2pO2SaO2SpO2vitals_tempccbc_hemoglobinbmp_sodiumbmp_bicarbonatebmp_creatininesofa_past_overall_24hrsofa_past_cardiovascular_24hrp50Gender
9190805eicu60.00125.9172.742.2124986.8840283.291667Charlson3.00.0White7.4139.078.096.096.038.112.8138.021.00.8006.01.024.153464Male
12141654eicu65.0077.1185.422.43029293.04513982.833333Charlson8.00.0White7.2845.0130.095.095.036.69.6132.029.05.1306.01.043.878585Male
13155590eicu44.00262.4190.572.30592230.30347215.458333Charlson0.00.0White7.2942.096.096.098.036.811.4138.023.05.0808.01.022.846133Male
18195394eicu67.00107.1179.133.3886073.9763893.958333Charlson3.00.0White7.3439.069.092.094.036.914.4145.021.01.1307.01.025.006964Male
19138012eicu62.00151.4167.653.89864512.7680564.833333Charlson4.00.0White7.3162.0117.097.097.038.39.7137.824.41.0742.01.032.458224Male
27130585eicu85.0188.0167.031.5536599.9194441.541667Charlson6.00.0Black7.5048.0140.098.099.036.611.7142.040.01.2706.01.025.704131Female
30132653eicu59.0088.7172.729.73986233.21388918.166667Charlson3.00.0White7.2355.096.096.097.036.112.1138.024.01.3301.01.026.632389Male
34163687eicu51.0147.9162.618.11734023.1000004.166667Charlson1.00.0White7.2412.0112.096.098.037.65.3137.06.00.8705.01.026.653822Female
41145590eicu61.0095.1182.928.4284457.9958333.833333Charlson3.00.0White7.3346.0115.097.099.036.79.2139.425.81.3682.00.021.114107Male
46176389eicu51.0053.0175.317.2469391.2763891.291667Charlson1.01.0White7.2548.078.093.078.035.68.8146.014.00.5009.01.048.903332Male
hospital_admission_idsource_dbadmission_agesex_femaleweight_admissionheight_admissionBMI_admissionlos_hospitallos_ICUcomorbidity_score_namecomorbidity_score_valuein_hospital_mortalityrace_ethnicitypHpCO2pO2SaO2SpO2vitals_tempccbc_hemoglobinbmp_sodiumbmp_bicarbonatebmp_creatininesofa_past_overall_24hrsofa_past_cardiovascular_24hrp50Gender
434132738149eicu78.0072.5167.625.81011713.5409727.291667Charlson4.00.0White7.30341.270.092.096.036.910.4133.025.01.275.01.021.676186Male
434142721917eicu47.0064.7180.319.9027384.4784721.458333Charlson2.00.0White7.34434.587.096.099.037.08.5130.015.01.291.00.015.973281Male
434162720045eicu61.00118.4170.240.87263121.74652814.500000Charlson4.00.0White7.36935.9144.099.096.036.910.6136.022.03.406.01.044.591011Male
434182728641eicu59.0053.7160.020.9765624.3236110.708333Charlson8.01.0Black7.34234.060.089.094.037.38.7139.018.04.515.01.021.745186Male
434212723336eicu61.0077.2175.325.1219573.5791673.583333Charlson7.00.0Black7.48529.650.088.095.036.910.6140.025.01.494.00.016.876379Male
434232741806eicu59.0076.2177.824.1041300.5763890.291667Charlson1.01.0White6.99060.074.084.094.032.511.4125.015.03.6313.01.026.819063Male
434252732191eicu80.0063.0165.123.1124727.8923611.708333Charlson7.01.0White7.42227.350.087.092.036.18.0145.023.00.838.00.020.310142Male
434262724336eicu59.00180.0175.358.5745113.4131941.416667Charlson2.00.0White7.39828.161.091.093.036.715.6130.019.00.814.01.023.493600Male
434312727434eicu62.0065.1167.623.1757059.8041673.083333Charlson2.00.0White7.26245.888.095.097.037.012.0146.023.00.962.02.024.413023Male
434322721288eicu67.00155.2172.752.03637618.18680611.333333Charlson6.00.0White7.37467.297.097.095.036.811.6145.036.01.901.00.032.740175Male